{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### News"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Placeholder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Placeholder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Unsloth"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
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        },
        "id": "QmUBVEnvCDJv",
        "outputId": "c36582a5-ee6c-483c-8a5b-9ec43430d2af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "==((====))==  Unsloth 2024.9.post4: Fast Llama patching. Transformers = 4.44.2.\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.4.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.28.post1. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
        },
        {
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              "model_id": "b03af9d3d3074c9fb43cc45d456b8e30",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/5.70G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
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            "text/plain": [
              "generation_config.json:   0%|          | 0.00/198 [00:00<?, ?B/s]"
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            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/50.6k [00:00<?, ?B/s]"
            ]
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        },
        {
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              "version_minor": 0
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            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"
            ]
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              "special_tokens_map.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
        "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
        "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
        "\n",
        "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
        "fourbit_models = [\n",
        "    \"unsloth/mistral-7b-v0.3-bnb-4bit\",      # New Mistral v3 2x faster!\n",
        "    \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n",
        "    \"unsloth/llama-3-8b-bnb-4bit\",           # Llama-3 15 trillion tokens model 2x faster!\n",
        "    \"unsloth/llama-3-8b-Instruct-bnb-4bit\",\n",
        "    \"unsloth/llama-3-70b-bnb-4bit\",\n",
        "    \"unsloth/Phi-3-mini-4k-instruct\",        # Phi-3 2x faster!\n",
        "    \"unsloth/Phi-3-medium-4k-instruct\",\n",
        "    \"unsloth/mistral-7b-bnb-4bit\",\n",
        "    \"unsloth/gemma-7b-bnb-4bit\",             # Gemma 2.2x faster!\n",
        "] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/llama-3-8b-bnb-4bit\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXd9bTZd1aaL"
      },
      "source": [
        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bZsfBuZDeCL",
        "outputId": "b2a0373b-a721-4170-d7d3-8e6f6a33b0d5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth 2024.9.post4 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
        "    lora_alpha = 16,\n",
        "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
        "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,  # We support rank stabilized LoRA\n",
        "    loftq_config = None, # And LoftQ\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vITh0KVJ10qX"
      },
      "source": [
        "<a name=\"Data\"></a>\n",
        "### Data Prep\n",
        "We now use the Alpaca dataset from [vicgalle](https://huggingface.co/datasets/vicgalle/alpaca-gpt4), which is a version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html) generated from GPT4. You can replace this code section with your own data prep."
      ]
    },
    {
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        "colab": {
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        },
        "id": "HvOPfPnet76H",
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      "outputs": [
        {
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            "text/plain": [
              "README.md:   0%|          | 0.00/3.38k [00:00<?, ?B/s]"
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          },
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            "text/plain": [
              "Generating train split:   0%|          | 0/52002 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['instruction', 'input', 'output', 'text']\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"vicgalle/alpaca-gpt4\", split=\"train\")\n",
        "print(dataset.column_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xg4_dG-m0Cz4"
      },
      "source": [
        "One issue is this dataset has multiple columns. For `Ollama` and `llama.cpp` to function like a custom `ChatGPT` Chatbot, we must only have 2 columns - an `instruction` and an `output` column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTQR4jrDMcJf",
        "outputId": "d8b592db-cdf2-49e6-990d-3a493b0770ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['instruction', 'input', 'output', 'text']\n"
          ]
        }
      ],
      "source": [
        "print(dataset.column_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MwEbRFl0Mf3E"
      },
      "source": [
        "To solve this, we shall do the following:\n",
        "* Merge all columns into 1 instruction prompt.\n",
        "* Remember LLMs are text predictors, so we can customize the instruction to anything we like!\n",
        "* Use the `to_sharegpt` function to do this column merging process!\n",
        "\n",
        "For example below in our [Titanic CSV finetuning notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb), we merged multiple columns in 1 prompt:\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/unslothai/unsloth/nightly/images/Merge.png\" height=\"100\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w61VJ7rQM8jT"
      },
      "source": [
        "To merge multiple columns into 1, use `merged_prompt`.\n",
        "* Enclose all columns in curly braces `{}`.\n",
        "* Optional text must be enclused in `[[]]`. For example if the column \"Pclass\" is empty, the merging function will not show the text and skp this. This is useful for datasets with missing values.\n",
        "* You can select every column, or a few!\n",
        "* Select the output or target / prediction column in `output_column_name`. For the Alpaca dataset, this will be `output`.\n",
        "\n",
        "To make the finetune handle multiple turns (like in ChatGPT), we have to create a \"fake\" dataset with multiple turns - we use `conversation_extension` to randomnly select some conversations from the dataset, and pack them together into 1 conversation."
      ]
    },
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            "70bb803085a2457092d4642a3baf8f54",
            "14c60f02caa441b7a5ce09ffd770f5a9",
            "ea6a29aacd804453863970f0e4bfb4ba",
            "1e6693fba36444749c5190ae984b7761",
            "5e1f0774a45141149ba3a9a62e2d50f1"
          ]
        },
        "id": "jZxeGSeX0CR8",
        "outputId": "6486785e-69cc-4434-bef0-f5360ab2ac1a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4dac566fd3ee404a8308026caf52e165",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Merging columns:   0%|          | 0/52002 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4a7639181756463fa0d2bdcddb0ee460",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Converting to ShareGPT:   0%|          | 0/52002 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "886ccb525cb149dfb49be917882f4ae3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Flattening the indices:   0%|          | 0/52002 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4ef743732002497395f3c2215713eb3e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Flattening the indices:   0%|          | 0/52002 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5edc00f7efce452e82b713120068d5ce",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Flattening the indices:   0%|          | 0/52002 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "18b7f26e9cc74aa7a3f01b7af539c4ba",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extending conversations:   0%|          | 0/52002 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from unsloth import to_sharegpt\n",
        "\n",
        "dataset = to_sharegpt(\n",
        "    dataset,\n",
        "    merged_prompt=\"{instruction}[[\\nYour input is:\\n{input}]]\",\n",
        "    output_column_name=\"output\",\n",
        "    conversation_extension=3,  # Select more to handle longer conversations\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Kh90vpD1jYJ"
      },
      "source": [
        "Finally use `standardize_sharegpt` to fix up the dataset!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d08ffe495f9649cd99fd13e38fa09aa5",
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            "2748bc11780947038af15b94cdc0565a",
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            "91b8e492c8e14eba9e9cd3c857e464ac",
            "2342b7593577476ebc15b5b0b7c93d37",
            "890767e37b054db5b6b2df4bb2ad351d",
            "e9c0e9a6545c49b9895f751b6c738974"
          ]
        },
        "id": "ZPwDXBvP1g8S",
        "outputId": "c400321c-0a47-47c3-d84c-e9ba524740a6"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d08ffe495f9649cd99fd13e38fa09aa5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Standardizing format:   0%|          | 0/52002 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from unsloth import standardize_sharegpt\n",
        "\n",
        "dataset = standardize_sharegpt(dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GThrcKACxTe2"
      },
      "source": [
        "### Customizable Chat Templates\n",
        "\n",
        "You also need to specify a chat template. Previously, you could use the Alpaca format as shown below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MVBanRIJRAcQ"
      },
      "outputs": [],
      "source": [
        "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "{}\n",
        "\n",
        "### Input:\n",
        "{}\n",
        "\n",
        "### Response:\n",
        "{}\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VTzZ5oZrxkFz"
      },
      "source": [
        "Now, you have to use `{INPUT}` for the instruction and `{OUTPUT}` for the response.\n",
        "\n",
        "We also allow you to use an optional `{SYSTEM}` field. This is useful for Ollama when you want to use a custom system prompt (also like in ChatGPT).\n",
        "\n",
        "You can also not put a `{SYSTEM}` field, and just put plain text.\n",
        "\n",
        "```python\n",
        "chat_template = \"\"\"{SYSTEM}\n",
        "USER: {INPUT}\n",
        "ASSISTANT: {OUTPUT}\"\"\"\n",
        "```\n",
        "\n",
        "Use below if you want to use the Llama-3 prompt format. You must use the `instruct` and not the `base` model if you use this!\n",
        "```python\n",
        "chat_template = \"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
        "\n",
        "{SYSTEM}<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
        "\n",
        "{INPUT}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
        "\n",
        "{OUTPUT}<|eot_id|>\"\"\"\n",
        "```\n",
        "\n",
        "For the ChatML format:\n",
        "```python\n",
        "chat_template = \"\"\"<|im_start|>system\n",
        "{SYSTEM}<|im_end|>\n",
        "<|im_start|>user\n",
        "{INPUT}<|im_end|>\n",
        "<|im_start|>assistant\n",
        "{OUTPUT}<|im_end|>\"\"\"\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EK-_ncj-RCNy"
      },
      "source": [
        "The issue is the Alpaca format has 3 fields, whilst OpenAI style chatbots must only use 2 fields (instruction and response). That's why we used the `to_sharegpt` function to merge these columns into 1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "a1d70bbc33a64c23a63f773abc5e22e6",
            "84f9e22160d74ec498f99e9c93b0c8bc",
            "cb89bc91831b431bba8b1f1a14ac05ec",
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            "1ce9fb4fb360443fbab1bc426b84e51e",
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            "cbf4c274668c4feeb407501754eb7bf1",
            "9249cef6e8824525a1e183498d9d41ac",
            "c37f06b87a9b41ba957ac760aad0ea48",
            "9c9436e28d9c4cfdb9a251ce1ee4cb39"
          ]
        },
        "id": "JOGaZf1sdLlr",
        "outputId": "ca525f00-aa3c-4563-af31-420f90e62acd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth: We automatically added an EOS token to stop endless generations.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a1d70bbc33a64c23a63f773abc5e22e6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/52002 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "chat_template = \"\"\"Below are some instructions that describe some tasks. Write responses that appropriately complete each request.\n",
        "\n",
        "### Instruction:\n",
        "{INPUT}\n",
        "\n",
        "### Response:\n",
        "{OUTPUT}\"\"\"\n",
        "\n",
        "from unsloth import apply_chat_template\n",
        "\n",
        "dataset = apply_chat_template(\n",
        "    dataset,\n",
        "    tokenizer=tokenizer,\n",
        "    chat_template=chat_template,\n",
        "    # default_system_message = \"You are a helpful assistant\", << [OPTIONAL]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idAEIeSQ3xdS"
      },
      "source": [
        "<a name=\"Train\"></a>\n",
        "### Train the model\n",
        "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "dee51ee264e24b809be18f6d286ae874",
            "a9a39684291d4eb1b7312640feae1462",
            "d7435502d95f4355b37d2f3332b057f1",
            "2d15261428824594908bd697644d61ed",
            "b3a8a71922f341758e1adf0f951208bb",
            "7f107e2a1d274a2f9bec4acce979d2da",
            "0d5da7e017424aa0b7ff0d40abad1ecc",
            "4c4bc99385eb49dd95ad4b740b949e55",
            "f62d1a6521574459ae6fb1ebd71ad631",
            "62191c10c14c4728bf85a293d29d37ce",
            "ec56936c53984c85ade94df6ce443ad5"
          ]
        },
        "id": "95_Nn-89DhsL",
        "outputId": "27f7cc9d-8fd9-4111-ee2d-a5a434b30282"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dee51ee264e24b809be18f6d286ae874",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/52002 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "from unsloth import is_bfloat16_supported\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    train_dataset = dataset,\n",
        "    dataset_text_field = \"text\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dataset_num_proc = 2,\n",
        "    packing = False, # Can make training 5x faster for short sequences.\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_steps = 5,\n",
        "        max_steps = 60,\n",
        "        # num_train_epochs = 1, # For longer training runs!\n",
        "        learning_rate = 2e-4,\n",
        "        fp16 = not is_bfloat16_supported(),\n",
        "        bf16 = is_bfloat16_supported(),\n",
        "        logging_steps = 1,\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.01,\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        seed = 3407,\n",
        "        output_dir = \"outputs\",\n",
        "        report_to = \"none\", # Use this for WandB etc\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ejIt2xSNKKp",
        "outputId": "623e558b-a1c9-425b-feff-4dcdbc381d0c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPU = Tesla T4. Max memory = 14.748 GB.\n",
            "5.613 GB of memory reserved.\n"
          ]
        }
      ],
      "source": [
        "# @title Show current memory stats\n",
        "gpu_stats = torch.cuda.get_device_properties(0)\n",
        "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
        "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
        "print(f\"{start_gpu_memory} GB of memory reserved.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yqxqAZ7KJ4oL",
        "outputId": "fd7d79c2-68a7-4e3a-e567-adecc3b1be87"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 52,002 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 8 | Total steps = 60\n",
            " \"-____-\"     Number of trainable parameters = 41,943,040\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60/60 14:46, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.530300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.521200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.419800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.496600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.618100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.255100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.290100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.277400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.323600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.166000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.204200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.189000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.049100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.115200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.150700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>1.051400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>1.051000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>1.173100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.129600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.104200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>1.033500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>1.111600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.966300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>1.042500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.017800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>1.071600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>1.092100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.074600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>1.077500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.122200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>1.066400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>1.154400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.980800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>1.074700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>1.155100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>1.142200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>1.047400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>1.024000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.990800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.018900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>1.025600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>1.039400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>1.166000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>1.086500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>1.001300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>1.040000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>1.076200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>1.120800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>1.020300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.238000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>1.094100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>1.112800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>1.098000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.985200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>1.009800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>1.034200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>1.018500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>1.043500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.986100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.032200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCqnaKmlO1U9",
        "outputId": "38fed629-e855-4386-c150-8578e0a73a15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "910.2379 seconds used for training.\n",
            "15.17 minutes used for training.\n",
            "Peak reserved memory = 8.361 GB.\n",
            "Peak reserved memory for training = 2.748 GB.\n",
            "Peak reserved memory % of max memory = 56.692 %.\n",
            "Peak reserved memory for training % of max memory = 18.633 %.\n"
          ]
        }
      ],
      "source": [
        "# @title Show final memory and time stats\n",
        "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
        "used_percentage = round(used_memory / max_memory * 100, 3)\n",
        "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n",
        "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
        "print(\n",
        "    f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",
        ")\n",
        "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
        "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
        "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
        "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekOmTR1hSNcr"
      },
      "source": [
        "<a name=\"Inference\"></a>\n",
        "### Inference\n",
        "Let's run the model! Unsloth makes inference natively 2x faster as well! You should use prompts which are similar to the ones you had finetuned on, otherwise you might get bad results!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kR3gIAX-SM2q",
        "outputId": "40b5e499-1ad5-4907-a284-9416a36d010c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The next number in the Fibonacci sequence is 13.<|end_of_text|>\n"
          ]
        }
      ],
      "source": [
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "messages = [                    # Change below!\n",
        "    {\"role\": \"user\", \"content\": \"Continue the fibonacci sequence! Your input is 1, 1, 2, 3, 5, 8,\"},\n",
        "]\n",
        "input_ids = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    add_generation_prompt = True,\n",
        "    return_tensors = \"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
        "_ = model.generate(input_ids, streamer = text_streamer, max_new_tokens = 128, pad_token_id = tokenizer.eos_token_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrSvZObor0lY"
      },
      "source": [
        "Since we created an actual chatbot, you can also do longer conversations by manually adding alternating conversations between the user and assistant!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JcbFUWEyQVaE",
        "outputId": "517ed3fe-009e-4ebf-d233-2883e943de82"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "France's tallest tower is called the Eiffel Tower.<|end_of_text|>\n"
          ]
        }
      ],
      "source": [
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "messages = [                         # Change below!\n",
        "    {\"role\": \"user\",      \"content\": \"Continue the fibonacci sequence! Your input is 1, 1, 2, 3, 5, 8\"},\n",
        "    {\"role\": \"assistant\", \"content\": \"The fibonacci sequence continues as 13, 21, 34, 55 and 89.\"},\n",
        "    {\"role\": \"user\",      \"content\": \"What is France's tallest tower called?\"},\n",
        "]\n",
        "input_ids = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    add_generation_prompt = True,\n",
        "    return_tensors = \"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
        "_ = model.generate(input_ids, streamer = text_streamer, max_new_tokens = 128, pad_token_id = tokenizer.eos_token_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMuVrWbjAzhc"
      },
      "source": [
        "<a name=\"Save\"></a>\n",
        "### Saving, loading finetuned models\n",
        "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
        "\n",
        "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upcOlWe7A1vc",
        "outputId": "587e0389-0044-47a2-f691-581d262ea95c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('lora_model/tokenizer_config.json',\n",
              " 'lora_model/special_tokens_map.json',\n",
              " 'lora_model/tokenizer.json')"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.save_pretrained(\"lora_model\")  # Local saving\n",
        "tokenizer.save_pretrained(\"lora_model\")\n",
        "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
        "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEEcJ4qfC7Lp"
      },
      "source": [
        "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKX_XKs_BNZR",
        "outputId": "98ec2273-2e01-4062-c576-1ffb7b3afdb0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The sequence 1, 1, 2, 3, 5, 8 is a special sequence known as the Fibonacci sequence. The Fibonacci sequence is a series of numbers where each number is the sum of the two previous numbers, starting with 0 and 1. In this case, the sequence is 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, and so on. The Fibonacci sequence has many interesting properties and is widely studied in mathematics and computer science.<|end_of_text|>\n"
          ]
        }
      ],
      "source": [
        "if False:\n",
        "    from unsloth import FastLanguageModel\n",
        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "        model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        max_seq_length = max_seq_length,\n",
        "        dtype = dtype,\n",
        "        load_in_4bit = load_in_4bit,\n",
        "    )\n",
        "    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "pass\n",
        "\n",
        "messages = [                    # Change below!\n",
        "    {\"role\": \"user\", \"content\": \"Describe anything special about a sequence. Your input is 1, 1, 2, 3, 5, 8,\"},\n",
        "]\n",
        "input_ids = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    add_generation_prompt = True,\n",
        "    return_tensors = \"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
        "_ = model.generate(input_ids, streamer = text_streamer, max_new_tokens = 128, pad_token_id = tokenizer.eos_token_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQMjaNrjsU5_"
      },
      "source": [
        "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yFfaXG0WsQuE"
      },
      "outputs": [],
      "source": [
        "if False:\n",
        "    # I highly do NOT suggest - use Unsloth if possible\n",
        "    from peft import AutoPeftModelForCausalLM\n",
        "    from transformers import AutoTokenizer\n",
        "    model = AutoPeftModelForCausalLM.from_pretrained(\n",
        "        \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        load_in_4bit = load_in_4bit,\n",
        "    )\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XOFzC441vCtq"
      },
      "source": [
        "<a name=\"Ollama\"></a>\n",
        "### Ollama Support\n",
        "\n",
        "[Unsloth](https://github.com/unslothai/unsloth) now allows you to automatically finetune and create a [Modelfile](https://github.com/ollama/ollama/blob/main/docs/modelfile.md), and export to [Ollama](https://ollama.com/)! This makes finetuning much easier and provides a seamless workflow from `Unsloth` to `Ollama`!\n",
        "\n",
        "Let's first install `Ollama`!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NUxcyP_UfeLl",
        "outputId": "69972ce0-9caf-41fd-b19a-fa058521990b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">>> Installing ollama to /usr/local\n",
            ">>> Downloading Linux amd64 bundle\n",
            "############################################################################################# 100.0%\n",
            ">>> Creating ollama user...\n",
            ">>> Adding ollama user to video group...\n",
            ">>> Adding current user to ollama group...\n",
            ">>> Creating ollama systemd service...\n",
            "WARNING: Unable to detect NVIDIA/AMD GPU. Install lspci or lshw to automatically detect and install GPU dependencies.\n",
            ">>> The Ollama API is now available at 127.0.0.1:11434.\n",
            ">>> Install complete. Run \"ollama\" from the command line.\n"
          ]
        }
      ],
      "source": [
        "!curl -fsSL https://ollama.com/install.sh | sh"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCv4vXHd61i7"
      },
      "source": [
        "Next, we shall save the model to GGUF / llama.cpp\n",
        "\n",
        "We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
        "\n",
        "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
        "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
        "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
        "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n",
        "\n",
        "We also support saving to multiple GGUF options in a list fashion! This can speed things up by 10 minutes or more if you want multiple export formats!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FqfebeAdT073",
        "outputId": "9d2292eb-1e31-4c88-9b44-5371e4104abf"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth: ##### The current model auto adds a BOS token.\n",
            "Unsloth: ##### Your chat template has a BOS token. We shall remove it temporarily.\n",
            "Unsloth: You have 1 CPUs. Using `safe_serialization` is 10x slower.\n",
            "We shall switch to Pytorch saving, which will take 3 minutes and not 30 minutes.\n",
            "To force `safe_serialization`, set it to `None` instead.\n",
            "Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded\n",
            "model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab.\n",
            "Unsloth: Will remove a cached repo with size 5.7G\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 5.49 out of 12.67 RAM for saving.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 47%|████▋     | 15/32 [00:01<00:01,  9.79it/s]We will save to Disk and not RAM now.\n",
            "100%|██████████| 32/32 [01:41<00:00,  3.18s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Saving tokenizer... Done.\n",
            "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n",
            "Unsloth: Saving model/pytorch_model-00001-of-00004.bin...\n",
            "Unsloth: Saving model/pytorch_model-00002-of-00004.bin...\n",
            "Unsloth: Saving model/pytorch_model-00003-of-00004.bin...\n",
            "Unsloth: Saving model/pytorch_model-00004-of-00004.bin...\n",
            "Done.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth: Converting llama model. Can use fast conversion = False.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth: Conversion from QLoRA to GGUF information\n",
            "   \\\\   /|    [0] Installing llama.cpp will take 3 minutes.\n",
            "O^O/ \\_/ \\    [1] Converting HF to GGUF 16bits will take 3 minutes.\n",
            "\\        /    [2] Converting GGUF 16bits to ['q8_0'] will take 10 minutes each.\n",
            " \"-____-\"     In total, you will have to wait at least 16 minutes.\n",
            "\n",
            "Unsloth: [0] Installing llama.cpp. This will take 3 minutes...\n",
            "Unsloth: [1] Converting model at model into q8_0 GGUF format.\n",
            "The output location will be ./model/unsloth.Q8_0.gguf\n",
            "This will take 3 minutes...\n",
            "INFO:hf-to-gguf:Loading model: model\n",
            "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n",
            "INFO:hf-to-gguf:Exporting model...\n",
            "INFO:hf-to-gguf:gguf: loading model weight map from 'pytorch_model.bin.index.json'\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00001-of-00004.bin'\n",
            "INFO:hf-to-gguf:token_embd.weight,           torch.float16 --> Q8_0, shape = {4096, 128256}\n",
            "INFO:hf-to-gguf:blk.0.attn_q.weight,         torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.0.attn_k.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.0.attn_v.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.0.attn_output.weight,    torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.0.ffn_gate.weight,       torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.0.ffn_up.weight,         torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.0.ffn_down.weight,       torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.0.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.0.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_q.weight,         torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_k.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.1.attn_v.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.1.attn_output.weight,    torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.1.ffn_gate.weight,       torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.1.ffn_up.weight,         torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.1.ffn_down.weight,       torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.1.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_q.weight,         torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_k.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.2.attn_v.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.2.attn_output.weight,    torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.2.ffn_gate.weight,       torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.2.ffn_up.weight,         torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.2.ffn_down.weight,       torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.2.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_q.weight,         torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_k.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.3.attn_v.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.3.attn_output.weight,    torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.3.ffn_gate.weight,       torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.3.ffn_up.weight,         torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.3.ffn_down.weight,       torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.3.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_q.weight,         torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_k.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.4.attn_v.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.4.attn_output.weight,    torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.4.ffn_gate.weight,       torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.4.ffn_up.weight,         torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.4.ffn_down.weight,       torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.4.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_q.weight,         torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_k.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.5.attn_v.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.5.attn_output.weight,    torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.5.ffn_gate.weight,       torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.5.ffn_up.weight,         torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.5.ffn_down.weight,       torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.5.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_q.weight,         torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_k.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.6.attn_v.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.6.attn_output.weight,    torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.6.ffn_gate.weight,       torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.6.ffn_up.weight,         torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.6.ffn_down.weight,       torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.6.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_q.weight,         torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_k.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.7.attn_v.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.7.attn_output.weight,    torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.7.ffn_gate.weight,       torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.7.ffn_up.weight,         torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.7.ffn_down.weight,       torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.7.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_q.weight,         torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_k.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.8.attn_v.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.8.attn_output.weight,    torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.8.ffn_gate.weight,       torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.8.ffn_up.weight,         torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.8.ffn_down.weight,       torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.8.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00002-of-00004.bin'\n",
            "INFO:hf-to-gguf:blk.9.attn_q.weight,         torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.9.attn_k.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.9.attn_v.weight,         torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.9.attn_output.weight,    torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.9.ffn_gate.weight,       torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.9.ffn_up.weight,         torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.9.ffn_down.weight,       torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.9.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.9.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.10.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.10.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.10.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.10.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.10.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.10.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.11.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.11.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.11.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.11.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.11.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.11.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.12.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.12.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.12.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.12.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.12.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.12.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.13.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.13.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.13.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.13.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.13.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.13.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.14.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.14.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.14.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.14.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.14.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.14.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.15.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.15.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.15.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.15.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.15.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.15.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.16.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.16.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.16.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.16.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.16.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.16.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.17.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.17.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.17.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.17.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.17.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.17.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.18.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.18.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.18.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.18.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.18.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.18.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.19.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.19.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.19.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.19.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.19.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.19.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.20.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.20.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.20.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00003-of-00004.bin'\n",
            "INFO:hf-to-gguf:blk.20.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.20.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.20.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.21.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.21.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.21.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.21.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.21.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.21.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.22.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.22.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.22.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.22.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.22.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.22.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.23.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.23.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.23.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.23.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.23.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.23.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.24.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.24.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.24.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.24.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.24.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.24.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.25.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.25.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.25.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.25.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.25.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.25.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.26.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.26.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.26.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.26.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.26.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.26.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.27.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.27.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.27.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.27.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.27.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.27.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.28.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.28.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.28.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.28.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.28.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.28.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.29.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.29.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.29.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.29.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.29.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.29.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.30.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.30.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.30.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.30.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.30.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.30.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_q.weight,        torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_k.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.31.attn_v.weight,        torch.float16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.31.attn_output.weight,   torch.float16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.31.ffn_gate.weight,      torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.31.ffn_up.weight,        torch.float16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00004-of-00004.bin'\n",
            "INFO:hf-to-gguf:blk.31.ffn_down.weight,      torch.float16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.31.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:output_norm.weight,          torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:output.weight,               torch.float16 --> Q8_0, shape = {4096, 128256}\n",
            "INFO:hf-to-gguf:Set meta model\n",
            "INFO:hf-to-gguf:Set model parameters\n",
            "INFO:hf-to-gguf:gguf: context length = 8192\n",
            "INFO:hf-to-gguf:gguf: embedding length = 4096\n",
            "INFO:hf-to-gguf:gguf: feed forward length = 14336\n",
            "INFO:hf-to-gguf:gguf: head count = 32\n",
            "INFO:hf-to-gguf:gguf: key-value head count = 8\n",
            "INFO:hf-to-gguf:gguf: rope theta = 500000.0\n",
            "INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-05\n",
            "INFO:hf-to-gguf:gguf: file type = 7\n",
            "INFO:hf-to-gguf:Set model tokenizer\n",
            "INFO:gguf.vocab:Adding 280147 merge(s).\n",
            "INFO:gguf.vocab:Setting special token type bos to 128000\n",
            "INFO:gguf.vocab:Setting special token type eos to 128001\n",
            "INFO:gguf.vocab:Setting special token type pad to 128255\n",
            "INFO:gguf.vocab:Setting chat_template to {{ 'Below are some instructions that describe some tasks. Write responses that appropriately complete each request.' }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '\n",
            "\n",
            "### Instruction:\n",
            "' + message['content'] }}{% elif message['role'] == 'assistant' %}{{ '\n",
            "\n",
            "### Response:\n",
            "' + message['content'] + '<|end_of_text|>' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '\n",
            "\n",
            "### Response:\n",
            "' }}{% endif %}\n",
            "INFO:hf-to-gguf:Set model quantization version\n",
            "INFO:gguf.gguf_writer:Writing the following files:\n",
            "INFO:gguf.gguf_writer:model/unsloth.Q8_0.gguf: n_tensors = 291, total_size = 8.5G\n",
            "Writing: 100%|██████████| 8.53G/8.53G [03:05<00:00, 46.1Mbyte/s]\n",
            "INFO:hf-to-gguf:Model successfully exported to model/unsloth.Q8_0.gguf\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth: ##### The current model auto adds a BOS token.\n",
            "Unsloth: ##### We removed it in GGUF's chat template for you.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Conversion completed! Output location: ./model/unsloth.Q8_0.gguf\n",
            "Unsloth: Saved Ollama Modelfile to model/Modelfile\n"
          ]
        }
      ],
      "source": [
        "# Save to 8bit Q8_0\n",
        "if True: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
        "# Remember to go to https://huggingface.co/settings/tokens for a token!\n",
        "# And change hf to your username!\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
        "\n",
        "# Save to 16bit GGUF\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
        "\n",
        "# Save to q4_k_m GGUF\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n",
        "\n",
        "# Save to multiple GGUF options - much faster if you want multiple!\n",
        "if False:\n",
        "    model.push_to_hub_gguf(\n",
        "        \"hf/model\", # Change hf to your username!\n",
        "        tokenizer,\n",
        "        quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n",
        "        token = \"\", # Get a token at https://huggingface.co/settings/tokens\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7lk6l0CuPXS"
      },
      "source": [
        "We use `subprocess` to start `Ollama` up in a non blocking fashion! In your own desktop, you can simply open up a new `terminal` and type `ollama serve`, but in Colab, we have to use this hack!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mcP9omF_tN7Q"
      },
      "outputs": [],
      "source": [
        "import subprocess\n",
        "\n",
        "subprocess.Popen([\"ollama\", \"serve\"])\n",
        "import time\n",
        "\n",
        "time.sleep(3)  # Wait for a few seconds for Ollama to load!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "md3PExRLRhOc"
      },
      "source": [
        "`Ollama` needs a `Modelfile`, which specifies the model's prompt format. Let's print Unsloth's auto generated one:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h82vfNigRhiz",
        "outputId": "bcd91437-d4cf-47de-8905-475e3fc4deec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "FROM {__FILE_LOCATION__}\n",
            "\n",
            "TEMPLATE \"\"\"Below are some instructions that describe some tasks. Write responses that appropriately complete each request.{{ if .Prompt }}\n",
            "\n",
            "### Instruction:\n",
            "{{ .Prompt }}{{ end }}\n",
            "\n",
            "### Response:\n",
            "{{ .Response }}<|end_of_text|>\"\"\"\n",
            "\n",
            "PARAMETER stop \"<|eot_id|>\"\n",
            "PARAMETER stop \"<|start_header_id|>\"\n",
            "PARAMETER stop \"<|end_header_id|>\"\n",
            "PARAMETER stop \"<|end_of_text|>\"\n",
            "PARAMETER stop \"<|reserved_special_token_\"\n",
            "PARAMETER temperature 1.5\n",
            "PARAMETER min_p 0.1\n"
          ]
        }
      ],
      "source": [
        "print(tokenizer._ollama_modelfile)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6cipBJBudxv"
      },
      "source": [
        "We now will create an `Ollama` model called `unsloth_model` using the `Modelfile` which we auto generated!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SDTUJv_QiaVh",
        "outputId": "66fcae42-3792-4b52-eb42-d867d9f83d69"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25ltransferring model data ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠧ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠇ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠏ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gtransferring model data ⠴ 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            "creating new layer sha256:94728011329d3d304c40e235f81f1b75580e163036c07d98382dc5548d555a34 \n",
            "creating new layer sha256:95b5361453780fb5797ce5abfe9a330f5d33fdec13d2232ef1443ee0c3a86ecc \n",
            "creating new layer sha256:57675488fe3dd2a75da06ae97984c4ce6f382208e9d989c584b22ee395bab0d8 \n",
            "creating new layer sha256:e706dd26476841ded603017f70f5b99b5be356caa859878787bfc3898d547f08 \n",
            "writing manifest \n",
            "success \u001b[?25h\n"
          ]
        }
      ],
      "source": [
        "!ollama create unsloth_model -f ./model/Modelfile"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-KSoKTKQukba"
      },
      "source": [
        "And now we can do inference on it via `Ollama`!\n",
        "\n",
        "You can also upload to `Ollama` and try the `Ollama` Desktop app by heading to https://www.ollama.com/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkp0uMrNpYaW",
        "outputId": "38bb3bd7-4a29-4c81-e319-388dcd96a449"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:04.241326628Z\",\"message\":{\"role\":\"assistant\",\"content\":\"The\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:04.465575479Z\",\"message\":{\"role\":\"assistant\",\"content\":\" next\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:04.760101468Z\",\"message\":{\"role\":\"assistant\",\"content\":\" number\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:05.051240606Z\",\"message\":{\"role\":\"assistant\",\"content\":\" in\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:05.376545126Z\",\"message\":{\"role\":\"assistant\",\"content\":\" the\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:05.515751946Z\",\"message\":{\"role\":\"assistant\",\"content\":\" Fibonacci\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:05.658721744Z\",\"message\":{\"role\":\"assistant\",\"content\":\" sequence\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:05.795226527Z\",\"message\":{\"role\":\"assistant\",\"content\":\" after\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:05.923676364Z\",\"message\":{\"role\":\"assistant\",\"content\":\" \"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.053599585Z\",\"message\":{\"role\":\"assistant\",\"content\":\"8\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.187220374Z\",\"message\":{\"role\":\"assistant\",\"content\":\" is\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.316237671Z\",\"message\":{\"role\":\"assistant\",\"content\":\" \"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.448901764Z\",\"message\":{\"role\":\"assistant\",\"content\":\"13\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.585864644Z\",\"message\":{\"role\":\"assistant\",\"content\":\" (\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.712030586Z\",\"message\":{\"role\":\"assistant\",\"content\":\"the\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:06.835728964Z\",\"message\":{\"role\":\"assistant\",\"content\":\" sum\"},\"done\":false}\n",
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            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:07.212942126Z\",\"message\":{\"role\":\"assistant\",\"content\":\" previous\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:07.336569966Z\",\"message\":{\"role\":\"assistant\",\"content\":\" two\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:07.46094096Z\",\"message\":{\"role\":\"assistant\",\"content\":\" numbers\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:07.593857726Z\",\"message\":{\"role\":\"assistant\",\"content\":\").\"},\"done\":false}\n",
            "{\"model\":\"unsloth_model\",\"created_at\":\"2024-10-01T06:47:07.741203726Z\",\"message\":{\"role\":\"assistant\",\"content\":\"\"},\"done_reason\":\"stop\",\"done\":true,\"total_duration\":3741960321,\"load_duration\":48967410,\"prompt_eval_count\":47,\"prompt_eval_duration\":150430000,\"eval_count\":23,\"eval_duration\":3499634000}\n"
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      ],
      "source": [
        "!curl http://localhost:11434/api/chat -d '{ \\\n",
        "    \"model\": \"unsloth_model\", \\\n",
        "    \"messages\": [ \\\n",
        "        { \"role\": \"user\", \"content\": \"Continue the Fibonacci sequence: 1, 1, 2, 3, 5, 8,\" } \\\n",
        "    ] \\\n",
        "    }'"
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        "# ChatGPT interactive mode\n",
        "\n",
        "### ⭐ To run the finetuned model like in a ChatGPT style interface, first click the **| >_ |** button.\n",
        "![](https://raw.githubusercontent.com/unslothai/unsloth/nightly/images/Where_Terminal.png)\n",
        "\n",
        "---\n",
        "---\n",
        "---\n",
        "\n",
        "### ⭐ Then, type `ollama run unsloth_model`\n",
        "\n",
        "![](https://raw.githubusercontent.com/unslothai/unsloth/nightly/images/Terminal_Type.png)\n",
        "\n",
        "---\n",
        "---\n",
        "---\n",
        "### ⭐ And you have a ChatGPT style assistant!\n",
        "\n",
        "### Type any question you like and press `ENTER`. If you want to exit, hit `CTRL + D`\n",
        "![](https://raw.githubusercontent.com/unslothai/unsloth/nightly/images/Assistant.png)"
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